Romania tourism generating region

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The zero value indicates a random spatial pattern. The approaches employed are spatial decision support applications and spatial statistics support applications.

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Thus, the forms of spatial autocorrelation positive and negative for the two data sets could be identified for both cases — univariate and, respectively, bivariate analysis. In this list there are large cities, mountain and seashore resorts. The interpretation of results has placed an important emphasis on the significance of the identified spatial cross-correlations, as a basis for appropriate, differentiated tourism-support policies in the highly attractive tourist areas. In order to identify significant spatial associations of LAUs for each of the two variables considered for our analysis, the significance map has been created allowing the identifying of locations with significant local Moran index. Both data sets are processed in order to get aggregate data at LAU-2 level 2 , which means locality level. Two data sets comprise public data about all companies represented in the Romanian tourism industry in December The choice of this segment of tourist infrastructure has been mainly determined by the supply perspective of tourism, which points to large agglomerations of tourist companies able to bring benefits represented by externalities generated at destination increased income, cost reduction. The results indicate an uneven territorial distribution of tourism infrastructure compared to the location of tourist attractions, significant differences between the geographical distribution of the accommodation and foodservice companies and suggest differentiated policies for supporting tourism infrastructure, in accordance with the specific needs of the tourist areas.

In this list there are large cities, mountain and seashore resorts. The research is based on three working hypotheses, namely: H1: Even if Romania displays competitive advantages in terms of tourism infrastructure units, these are not evenly distributed in the territorial units counties, regions with important tourist attractions.

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Accordingly, our paper aims to reveal their distribution and resulted spatial agglomerations in Romania, as a background for rational decisions regarding the support that will be offered to the most relevant tourism destinations as well as the measures meant to enhance collaborative networks and competitiveness in the tourism activity-based agglomerations, creating synergies that can increase economic performance.

The main difference between the two statistics consists in the definition of the similarity index. In recent decades the supply perspective of tourism, focusing on large agglomerations of tourism companies that bring benefits in terms of positive externalities at destination, has been more and more emphasized.

Overall, the tourism industry had a 4.

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The country boasts 30 upland regions, islands,km of marked walking trails, including premium trails and more than quality-certified trails, as well as 70, km of long-distance cycle routes.

Most tourists travel by car 67 percentair 22 percent and coach 10 percentaccording to the same source. Thus, the forms of spatial autocorrelation positive and negative for the two data sets could be identified for both cases — univariate and, respectively, bivariate analysis.

H3: The specific spatial correlations between accommodation and foodservice infrastructure can suggest useful ideas for adequate policies in highly attractive tourist areas.

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This perspective, emphasized in studies published in recent decades e. Incoming tourism generated EUR When such areal units are used, the choropleth mapping of a variable portrays a pattern over space. The data sets have also been described. This configuration is a confirmation of the first category incorporating the most important tourist areas in Romania Black Sea, Delta of Danube, Prahova Valley, Bucovina, etc. The research questions our paper is focused on are: Which are the main patterns of the spatial distribution of accommodation and foodservice companies in Romania? As a result, various EU-funded programmes for incorporate priorities regarding tourism development: the Regional Operational Programme, the Economic Competitiveness Programme and the National Programme for Rural Development, their denominator being the regional dimension of tourism development. This source of data — at the lowest level of aggregation — has been chosen as a result of the fact that the statistical data offered by the National Institute of Statistics with regard to the economic activity in tourism are not available at this level, while a higher level of aggregation for this kind of analysis would not have been appropriate. The zero value indicates a random spatial pattern. GNTB estimates that this could increase to In addition, research results also suggest the possibility of cooperation between neighbouring hotels of a similar quality. The choice of this segment of tourist infrastructure has been mainly determined by the supply perspective of tourism, which points to large agglomerations of tourist companies able to bring benefits represented by externalities generated at destination increased income, cost reduction. The distribution of all companies represented in the foodservice sector is presented in Figure 2.

A tendency towards similarity or dissimilarity for neighbouring values on such a map can be directly taken as spatial autocorrelation. The results indicate an uneven territorial distribution of tourism infrastructure compared to the location of tourist attractions, significant differences between the geographical distribution of the accommodation and foodservice companies and suggest differentiated policies for supporting tourism infrastructure, in accordance with the specific needs of the tourist areas.

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